docs: def get_qout(self,
                   river_id_array=None,
                   date_search_start=None,
                   date_search_end=None,
                   time_index_start=None,
                   time_index_end=None,
                   time_index=None,
                   time_index_array=None,
                   daily=False,
                   pd_filter=None,
                   filter_mode="mean",
                   as_dataframe=False):
          """
          This method extracts streamflow data by a single river ID
          or by a river ID array. It has options to extract by date
          or by date index.
  
          Parameters
          ----------
          river_id_array: :obj:`numpy.array` or list or int, optional
              A single river ID or an array of river IDs.
          date_search_start: :obj:`datetime.datetime`, optional
              This is a datetime object with the date of the minimum date
              for starting.
          date_search_end: :obj:`datetime.datetime`, optional
              This is a datetime object with the date of the maximum date
              for ending.
          time_index_start: int, optional
              This is the index of the start of the time array subset.
              Useful for the old file version.
          time_index_end: int, optional
              This is the index of the end of the time array subset.
              Useful for the old file version.
          time_index: int, optional
              This is the index of time to return in the case that your
              code only wants one index. Used internally.
          time_index_array: list or :obj:`numpy.array`, optional
              This is used to extract the vales only for particular dates.
              This can be from the *get_time_index_range* function.
          daily: bool, optional
              If true, this will convert qout to daily average.
          pd_filter: str, optional
              This is a valid pandas resample frequency filter.
          filter_mode: str, optional
              You can get the daily average "mean" or the maximum "max".
              Default is "mean".
          as_dataframe: bool, optional
              Return as a pandas dataframe object. Default is False.
  
  
          Returns
          -------
          qout_array: :obj:`numpy.array`
              This is a 1D or 2D array or a single value depending on your
              input search.
  
  
          This example demonstrates how to retrieve the streamflow associated
          with the reach you are interested in::
  
              from RAPIDpy import RAPIDDataset
  
              path_to_rapid_qout = '/path/to/Qout.nc'
              river_id = 500
              with RAPIDDataset(path_to_rapid_qout) as qout_nc:
                  streamflow_array = qout_nc.get_qout(river_id)
  
          This example demonstrates how to retrieve the streamflow within a date
          range associated with the reach you are interested in::
  
              from RAPIDpy import RAPIDDataset
  
              path_to_rapid_qout = '/path/to/Qout.nc'
              river_id = 500
              with RAPIDDataset(path_to_rapid_qout) as qout_nc:
                  streamflow_array = qout_nc.get_qout(
                      river_id,
                      date_search_start=datetime(1985,1,1),
                      date_search_end=datetime(1985,2,4))
  
          """
          # get indices of where the streamflow data is
          riverid_index_list_subset = None
          if river_id_array is not None:
              if not hasattr(river_id_array, "__len__"):
                  river_id_array = [river_id_array]
              riverid_index_list_subset = \
                  self.get_subset_riverid_index_list(river_id_array)[0]
  
          return self.get_qout_index(riverid_index_list_subset,
                                     date_search_start,
                                     date_search_end,
                                     time_index_start,
                                     time_index_end,
                                     time_index,
                                     time_index_array,
                                     daily,
                                     pd_filter,
                                     filter_mode,
                                     as_dataframe)